Here's how it works: IBM's Watson will sift through a variety of consumer pictures, searching for items that are popular among consumers. Watson will go through millions of images, tagging the most liked and shared images uploaded online. Popular items may have a certain shape and cut to them, an attractive pattern, or the material being used may be more comfortable and durable when compared with something similar. The trick is to learn what the consumer likes, produce the item, and market it.

The hype surrounding autonomous vehicles is almost always positive, and that's understandable. The thought of actually seeing a Jetson's-like vehicle (minus the airborne features) is pretty exciting. It means we're living in the future. Ten years ago, the thought of a self-driving vehicle was just a concept, but already, companies have progressed at alarming leaps and bounds, paving the way for a future filled with driverless vehicles.

At this stage, the companies invested in ADAS (Advanced Driver Assist Systems) are more interested in collecting data.

While there are trial runs in place, these are in place to collect data. One of the more invested companies, Intel produces programmable logic devices (PLD), MAX® CPLDs, Cyclone®, and MAX 10 family of FPGAs and SoC, and other devices that can control everything from infotainment to the radar controlled braking systems. Xilinx, alternatively, offers Zynq UltraScale+ MPSoC as well as a plethora of other solutions in the ADAS arena.

Intel provides a brief overview of what the company expects from current ADAS trials.

At this deployment stage, the algorithms behind ADAS systems are designed to capture anomalies and store this information. Each anomaly or unique situation will be analyzed, and an appropriate response will be determined. There are several benefits that come along with having this sort of system in place.

1) The ADAS system will 'learn' the appropriate response for each unique situation.

2) By having different scenarios in place and learning more about the appropriate response to each, the ADAS system can react effectively and quickly in response to other familiar stimuli.

Developers are using Xilinx's Zynq-7000 all-programmable SoC to draw a 3-dimensional map of a face. The project will have a variety of applications.

It can be used as a safety and security measure. In addition to biometric trackers, companies can install a facial recognition tool to grant or deny access to certain employees.

Facial recognition will go hand-in-hand with any vehicle ADAS system. Car manufacturers can install the system to detect instances of driver drowsiness. If the vehicle's ADAS system detects drowsiness, it can activate an alarm system, prompting the driver into alertness.

Facial recognition can also be used in a virtual reality environment, a hands-free environment and in robotics.

The system works by creating a 3D map of the individual's face and head shape. It can also track and predict various facial expressions, including eyebrow contours, mouth shape, chin pose, and gaze direction.

The hardware behind these systems are enabled using FPGA. When it comes to automated financial trading, your success depends on the speed and accuracy of a transaction. FPGA are used to build systems which enable low-latency.

Xilinx's 7-series FPGA are used to power these systems. The Virtex-7 families, Kintex-7, and Zynq 7000 SoC are used to build financial trading platforms.

2) Machine learning

Image search engines are becoming more sophisticated with each passing day. Search engines are now built using deep neural networks, allowing machines to learn as they go along. Nowadays, image searches are faster and more accurate compared to what had been there ten years ago.

Xilinx's Virtex Ultrascale+ FPGA are used to enable the sophistication required by a heavy-duty machine learning platform.

3) Artificial Intelligence

The task of building a true, sentient being is not easy, but a lot of companies are taking up the challenge. Right now, companies are building robots that are specialized to handle a single type of task. By focusing on one special task, the robot can be built to handle all dynamics around that task, things ranging from different scenarios to learning opportunities.

4) Internal Applications

This goes along with the financial trading platform. Often, companies need software and hardware that is custom-built to meet the company's needs. These specialized systems are built to meet the needs of the company, depending on the budget on hand and the performance level that is required.

5) Healthcare Products

Healthcare will be one of the fastest growing markets in the world. Machines will be used along with healthcare services. Advanced healthcare machines are now built to provide diagnoses, saving patients the time and money of obtaining a second opinion.

The hardware behind ADAS systems will also require a high-end FPGA, preferably one that can handle and process large amounts of data in a matter of seconds and then make an accurate decision based on input from the outside world. An ADAS system will also be enabled with deep neural networks, allowing the system to learn from experience.

This is one of the reasons why companies like BMW, Google, Uber and Tesla are relying on hours and hours of prototype driving. As the ADAS system learns, it will know how to react to certain situations.

7) Drone advancement

There's talk that drones should operate so that it's like a flock of birds. Right now, lone drones are sent on high-profile missions, but a single drone is easier to target and eliminate. When there are multiple drones around, it is much easier to complete a mission.

8) Wireless networking

5G systems are already in test phase and will be introduced by the year 2020. 5G systems will be used to enable communication between IoT devices, ensuring speed, accuracy and low-latency.

Are you in the market for FPGA? Search through our index list to see if the part you are looking for is available.

Samsung Electronics Company and SK Hynix Inc are two of the top manufacturers of high-end chips. Combined, the two companies supply 74 percent of the global DRAM market and 47 percent of the global NAND flash sector.

For the longest time, Nvidia was known for producing the best graphics cards in the computer business. Any serious gamer would have had to invest in an Nvidia graphics card to play visually-intensive video games.

FPGA are major hardware pieces in vital healthcare equipment. When the word diagnostic imaging is mentioned, some of the first things that should come to your mind are X-ray scans, ultrasounds, PET, CT, and MRI scans.

Data acquisition cards are used to acquire the input, and then these are sent along to processing, where filtering and algorithmic processing enable image reconstruction.